Seasonal changes in water of River Chenab and its tributaries, Jammu and Kashmir, India
Bibliographic record
Abstract
This study involved the analysis of 13 physiochemical parameters (pH, temperature, electrical conductivity, total dissolved solids, turbidity, alkalinity, hardness, calcium (Ca), magnesium (Mg), dissolved oxygen, biological oxygen demand, sulfate and nitrate) and five heavy metals (chromium (Cr), zinc, arsenic, lead and cadmium) from the Chenab River and its tributaries (Neeru and Bichleri) at Ramban and Doda Districts, Jammu and Kashmir, India. The analysis was done in two different seasons – namely, summer (June) and winter (December) of 2022. The current investigation indicated that all the physiochemical parameters were within the permissible limit in both seasons except for a few parameters (calcium, magnesium, turbidity). Heavy metal analysis from both seasons revealed that all sampling sites were not contaminated with heavy metals except chromium, the concentration of which was found to be higher in all sites (except SXII) in the summer season. The detailed analysis involved the calculation of various water quality indices, which graded the water quality under the ‘good’ category according to the water quality index value, whereas the comprehensive pollution index and heavy metal pollution index values showed a moderate to high pollution level in water during the summer season. The study showed a seasonal variation in water quality parameters and thus encourages the need for regular monitoring of water quality to reduce the pollution level.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".